Risk Managment

Knowledge is power, and additional data can translate into insights. Risk management has always been a matter of measuring. The process aims to quantify the frequency of loss and multiply it by the severity of the damage. Therefore, data should be at the core of it, primarily since every transaction generates data and metadata which can be included in models. Yet, to be useful, it has to be correctly selected, filtered and analyzed.

Implementation of data science in an organization requires a dedicated strategy to avoid sub-par results and information overload. When done correctly, it can offer a competitive advantage, insights and even new ways to tackle old problems. In a nutshell, risk management through the lens of data science becomes the ability to transform data into action.